PoultryTalk: A Multi-modal Retrieval-Augmented Generation (RAG) System for Intelligent Poultry Management and Decision Support

TL;DR

PoultryTalk is a multi-modal AI system that provides real-time, expert-level poultry management advice through text and images, demonstrating high accuracy, user satisfaction, and potential for scalable decision support for farmers.

Contribution

This paper introduces PoultryTalk, a novel multi-modal RAG system that enhances poultry management support with real-time, context-aware advice using advanced AI models.

Findings

Achieved 84.0% semantic similarity on expert-verified queries.

Delivered 89.9% response accuracy in user evaluations.

Responded with an average latency of 3.6 seconds.

Abstract

The Poultry industry plays a vital role in global food security, yet small- and medium-scale farmers frequently lack timely access to expert-level support for disease diagnosis, nutrition planning, and management decisions. With rising climate stress, unpredictable feed prices, and persistent disease threats, poultry producers often struggle to make quick, informed decisions. Therefore, there is a critical need for intelligent, data-driven systems that can deliver reliable, on-demand consultation. This paper presents PoultryTalk, a novel multi-modal Retrieval-Augmented Generation (RAG) system designed to provide real-time expert guidance through text and image-based interaction. PoultryTalk uses OpenAI's text-embedding-3-small and GPT-4o to provide smart, context-aware poultry management advice from text, images, or questions. System usability and performance were evaluated using 200 expert-verified queries and feedback from 34 participants who submitted 267 queries to the PoultryTalk prototype. The expert-verified benchmark queries confirmed strong technical performance, achieving a semantic similarity of 84.0% and an average response latency of 3.6 seconds. Compared with OpenAI's GPT-4o, PoultryTalk delivered more accurate and reliable information related to poultry. Based on participants' evaluations, PoultryTalk achieved a response accuracy of 89.9%, with about 9.1% of responses rated as incorrect. A post-use survey indicated high user satisfaction: 95.6% of participants reported that the chatbot provided "always correct" and "mostly correct" answers. 82.6% indicated they would recommend the tool, and 17.4% responded "maybe." These results collectively demonstrate that PoultryTalk not only delivers accurate, contextually relevant information but also demonstrates strong user acceptance and scalability potential.